Individual Project #1

library(twitteR)

## Warning: package 'twitteR' was built under R version 4.2.2

library(rtweet)

## Warning: package 'rtweet' was built under R version 4.2.2

## 
## Attaching package: 'rtweet'

## The following object is masked from 'package:twitteR':
## 
##     lookup_statuses

library(tm)

## Warning: package 'tm' was built under R version 4.2.2

## Loading required package: NLP

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:twitteR':
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##     id, location

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(plotly)

## Warning: package 'plotly' was built under R version 4.2.2

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.2.2

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## Attaching package: 'ggplot2'

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## Attaching package: 'plotly'

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library(ggplot2)
library(RColorBrewer)
library(tidytext)

## Warning: package 'tidytext' was built under R version 4.2.2

library(stringr)
library(tidyr)

## Warning: package 'tidyr' was built under R version 4.2.2

library(magrittr)

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## Attaching package: 'magrittr'

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##     extract

library(wordcloud)

## Warning: package 'wordcloud' was built under R version 4.2.2

library(wordcloud2)

## Warning: package 'wordcloud2' was built under R version 4.2.2

Extract from twitter using your developer’s credentials. Choose any keyword you want. Get 10000 observations “excluding retweets.

CONSUMER_KEY <- "zVjxW3DritguyMEzypBKcJ3fT"
CONSUMER_SECRET <- "KH1WJQgp9UgJFyEpz0R1ctYAnoQPkOJCaZd08u0gUjt5e9oBdF"
ACCESS_TOKEN <-"1595389319673704448-Q61VFf4QSUEDydIS3rxhH8DZUDzkQV"
ACCESS_SECRET <- "Qxy4x9RC0duHTPGaV312p9NXXUh69TwBuyplZ7EjyEpq4"


setup_twitter_oauth(consumer_key = CONSUMER_KEY,
                    consumer_secret = CONSUMER_SECRET,
                    access_token = ACCESS_TOKEN,
                    access_secret = ACCESS_SECRET)

## [1] "Using direct authentication"

trendTwts <- searchTwitter("Philippines -filter:retweets", 
                          n = 10000, 
                         lang = "en", 
                          since = "2022-11-20", 
                          until = "2022-11-27",
                          retryOnRateLimit = 120)

trendTwtsDF <- twListToDF(trendTwts)
trendTwtsDF 

save(trendTwtsDF, file = "trendTwtsDF.Rdata") 


head(trendTwtsDF$text)

## [1] "The Philippines is eyeing to work closely with the member states of the Association of Southeast Asian Nations in a… https://t.co/shvNXeKxYH"
## [2] "The Philippines is eyeing to work closely with the member states of the Association of Southeast Asian Nations in a… https://t.co/aweXcOaSgo"
## [3] "@EZPZ_Val Even I didn't won in your Giveaways , I am here your Solid Follower in the Philippines ❤️ Godbless soon I… https://t.co/DBY8AHsnm9" 
## [4] "I paid 1$ to enter here at Mt. Palpalan Pagadian City Philippines - 1,882.5 feet https://t.co/v2K8tAcDjo"                                    
## [5] "my aunt came back from the philippines with fresh mangoes 😋 https://t.co/WDPGtAC7v9"                                                        
## [6] "The Philippines and Australia have committed to strengthening their bilateral foreign policy and defense cooperatio… https://t.co/zwpSbLog5C"

sapply(trendTwtsDF, function(x) sum(is.na(x)))

##          text     favorited favoriteCount     replyToSN       created 
##             0             0             0          5966             0 
##     truncated    replyToSID            id    replyToUID  statusSource 
##             0          6094             0          5966             0 
##    screenName  retweetCount     isRetweet     retweeted     longitude 
##             0             0             0             0          9917 
##      latitude 
##          9917

trendsDF <- trendTwtsDF %>%
select(screenName,text,created,statusSource)
save(trendsDF, file = "trendsDF.Rdata")

Plot the time series from the date created, with legends.

trendsDF %>%  
  group_by(1) %>%  
  summarise(max = max(created), min = min(created))                          

trendsDF %<>% 
  mutate(Created_At_Round = created%>% 
           round(units = 'hours') %>% 
           as.POSIXct())

trendsDF %>% pull(created) %>% min()

## [1] "2022-11-24 15:10:31 UTC"

trendsDF %>% pull(created) %>% max()                        

## [1] "2022-11-26 23:59:16 UTC"

  plt <- trendsDF %>% 
  dplyr::count(Created_At_Round) %>% 
  ggplot(mapping = aes(x = Created_At_Round, y = n)) +
  theme_light() +
  geom_line(aes(x = Created_At_Round, y = n, colour = "red") )+
  xlab(label = 'Date') +
  ylab(label = NULL) +
  ggtitle(label = "Number of Tweets per Hour")

  plt %>% ggplotly()             

trendsDF %>%  
  group_by(1) %>%  
 summarise(max = max(created), min = min(created))

trendsDF %<>% 
  mutate(Created_At_Round = created%>% 
           round(units = 'hours') %>% 
           as.POSIXct())

Plot a graph (any graph you want) based on the type of device - found in Source - that the user use. Include the legends.

encodeSource <- function(x) {
  if(grepl(">Twitter for iPhone</a>", x)){
    "iphone"
  }else if(grepl(">Twitter for iPad</a>", x)){
    "ipad"
  }else if(grepl(">Twitter for Android</a>", x)){
    "android"
  } else if(grepl(">Twitter Web Client</a>", x)){
    "Web"
  } else if(grepl(">Twitter for Windows Phone</a>", x)){
    "windows phone"
  }else if(grepl(">dlvr.it</a>", x)){
    "dlvr.it"
  }else if(grepl(">IFTTT</a>", x)){
    "ifttt"
  }else if(grepl(">Facebook</a>", x)){  .
    "facebook"
  }else {
    "others"
  } 
}

 trendsDF$tweetSource = sapply(trendsDF$statusSource, 
                              encodeSource)

tweet_appSource <- trendsDF %>% 
  select(tweetSource) %>%
  group_by(tweetSource) %>%
  summarize(count=n()) %>%
  arrange(desc(count)) 

deviceSource <- ggplot(trendsDF[trendsDF$tweetSource != 'others',], aes(tweetSource, 
  fill = tweetSource)) +
  geom_bar() +
  theme(legend.position="right",
        axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  ylab("Number of tweets") +
  ggtitle("Tweets by Source") 
deviceSource 

     tweet_appScreen <- trendsDF %>%
  select(screenName) %>%
  group_by(screenName) %>%
  summarize(count=n()) %>%
  arrange(desc(count))                   

namesCorpus <- Corpus(VectorSource(trendsDF$screenName))  
class(trendsDF$screenName)

## [1] "character"

library(wordcloud2)
wordcloud2(data=tweet_appScreen, 
           size=0.8, 
           color='random-dark',
           shape = 'pentagon')